Tax Incidence and Tax Avoidance*
Bibliographic record
Abstract
ABSTRACT Economists broadly agree that the economic burden of corporate taxes is not entirely borne by shareholders but also borne in part by employees and consumers. We examine corporate tax avoidance in a setting where shareholders do not bear the entire economic burden of the corporate tax. We show that the relation between corporate tax incidence and corporate tax avoidance depends on the elasticity of labor supply, the productivity of capital relative to labor, and the tax deductibility of labor and capital. These forces operate through two channels (firm scale and input mix), making the actual association between tax avoidance and incidence an empirical question. We find that firms whose shareholders bear less of the economic burden of corporate taxes engage in less avoidance. Our findings suggest that maximizing after‐tax profits might entail less tax avoidance if shareholders do not entirely bear the corporate tax burden. In particular, when the incidence of the corporate tax falls on the firm, firms avoid more taxes. This tendency is stronger if firms use a higher level of capital input, if the deductibility of the cost of capital investment is limited, if firms have high capital productivity, or if tax enforcement is strong.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".